AI Agent Engineering: Build Production Ready Agentic Systems with LangChain, MCP, FastAPI

What you'll learn
✓ Understand agentic systems and clearly differentiate them from traditional LLM applications.
✓ Design AI agent architectures using modern agentic patterns, memory, and tool based reasoning.
✓ Build production AI agents with LangChain, LangGraph, Deep Agents, MCP, RAG pipelines, and multi-agent collaboration.
✓ Secure, evaluate, and monitor AI agents using guardrails, Langfuse, observability, authentication, and performance metrics.
✓ Deploy scalable agentic systems to the cloud using Docker, FastAPI, and real world production workflows.

Requirements
Basic Python programming knowledge (functions, classes, virtual environments)
Familiarity with REST APIs and JSON
Fundamental understanding of LLMs or prior experience using ChatGPT or similar tools
A laptop or desktop with internet access
Willingness to install Python, VS Code, Pycharm and required libraries

Description
AI agents are rapidly evolving from simple chatbots into powerful autonomous systems capable of reasoning, planning, using tools, collaborating with other agents, and operating on real world enterprise data. Modern organizations are increasingly adopting agentic systems to automate workflows, enhance decision making, and build intelligent products.

This course is designed to help you become an AI Agent Engineer.

In AI Agent Engineering: Build Production Ready Agentic Systems, you will learn how to design, build, secure, and deploy modern AI agents using LangChain, MCP (Model Context Protocol), RAG pipelines, multi-agent architectures, and FastAPI. You will also gain a clear understanding of what agentic systems are and how they differ from traditional LLM based applications.

Unlike many introductory AI courses, this program focuses on real engineering practices. You will move beyond prompt based demos and explore professional agent architectures, including planning, memory, tool execution, and agent collaboration. You'll build MCP servers to connect agents with external tools and context, implement retrieval augmented generation for enterprise knowledge, and create scalable APIs for your agents using FastAPI.

The course emphasizes production readiness. You will learn how to add guardrails, observability, authentication, and evaluation to your agents, helping you build systems that are reliable, secure, and maintainable. Through hands on projects, you will develop autonomous task agents, enterprise knowledge agents, and multi-agent workflows that reflect real industry use cases.

By the end of this course, you won't just understand how AI agents work, you'll know how to engineer complete agentic systems, deploy them to cloud environments, and operate them in production.

Whether you are a software developer, backend engineer, full-stack developer, or AI practitioner, this course will give you the practical skills needed to move from basic LLM applications to production grade autonomous systems.

If you're ready to go beyond chatbots and start building real world AI agents, this course is your complete guide.

Who this course is for
Software developers and backend engineers who want to build real world AI agents and agentic systems
Full-stack developers looking to add production-grade AI agent skills to their toolkit
AI/ML practitioners who want hands-on experience with LangChain, MCP, RAG, and multi-agent architectures
Technical architects interested in designing scalable, secure agentic systems
Developers moving beyond basic LLM apps and chatbots into autonomous AI systems
Professionals preparing for advanced AI engineering or agent architecture roles

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